Olive oils may be commercialized as intense, medium or light, according to the intensity perception of fruitiness, bitterness and pungency attributes, assessed by a sensory panel. In this work, the capability of an electronic tongue to correctly classify olive oils according to the sensory intensity perception levels was evaluated. Cross-sensitivity and non-specific lipid polymeric membranes were used as sensors. The sensor device was firstly tested using quinine monohydrochloride standard solutions. Mean sensitivities of 14±2 to 25±6 mV/decade, depending on the type of plasticizer used in the lipid membranes, were obtained showing the device capability for evaluating bitterness. Then, linear discriminant models based on sub-sets of sensors, selected by a meta-heuristic simulated annealing algorithm, were established enabling to correctly classify 91% of olive oils according to their intensity sensory grade (leave-one-out cross-validation procedure). This capability was further evaluated using a repeated K-fold cross-validation procedure, showing that the electronic tongue allowed an average correct classification of 80% of the olive oils used for internal-validation. So, the electronic tongue can be seen as a taste sensor, allowing differentiating olive oils with different sensory intensities, and could be used as a preliminary, complementary and practical tool for panelists during olive oil sensory analysis.

The intensities of the gustatory attributes of table olives is one of the sensory set of parameters evaluated by trained sensory panels accordingly to the recommendations of the International Olive Council. However this is an expensive and time-consuming process that only allows the evaluation of a limited number of samples per day. So, an electronic tongue coupled with multivariate statistical tools, is proposed for assessing the median intensities of acid, bitter and salty tastes perceived in table olives. The results showed that the device, coupled with linear discriminant analysis, could be used as a taste sensor, allowing classifying aqueous standard solutions according to the three basic tastes (repeated K-fold cross-validation: 98% ± 3% of correct classifications) based on the electrochemical signals of 5 sensors. It was demonstrated that the taste sensor with multiple linear regression models, enabled quantifying the median intensities of the three basic tastes (repeated K-fold cross-validation: R2 ? 0.96 ± 0.04) perceived in table olives by a trained sensory panel, based on the potentiometric fingerprints (2125 signal profiles) of aqueous olive pastes and brines. The overall satisfactory results showed the electronic tongue potential to assess the intensities of gustatory attributes of table olives, formerly only achievable by sensory panels.This work was financially supported by Project POCI-01–0145-FEDER-006984 – Associate Laboratory LSRE-LCM and by Project UID/QUI/00616/2013 – CQ-VR both funded by FEDER - Fundo Europeu de Desenvolvimento Regional through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia, Portugal. Strategic funding of UID/BIO/04469/2013 unit is also acknowledged. Nuno Rodrigues thanks FCT, POPH-QREN and FSE for the Ph.D. Grant (SFRH/BD/104038/2014)